Cori¶

login: ssh $my_username@cori.nersc.gov

interactive login:

# for Haswell:
salloc --partition=debug --nodes=1 --time=30:00 -C haswell

# for KNL:
salloc --partition=debug --nodes=1 --time=30:00 -C knl

Compute time:

Check hours of compute usage at https://nim.nersc.gov/

File system:

Overview: https://docs.nersc.gov/filesystems/
home directory: /global/homes/$my_username
scratch directory: /global/cscratch1/sd/$my_username
Check your individual disk usage with myquota
Check the group disk usage with prjquota projectID, i.e. prjquota m2833 or prjquota acme

Archive:

NERSC uses HPSS with the commands hsi and htar
overview: https://docs.nersc.gov/filesystems/archive/
E3SM uses zstash

Cori-Haswell¶

Since Cori’s Haswell and KNL nodes have different configuration options and compilers, they are treated as separate supported machines in compass.

config options¶

Here are the default config options added when you choose -m cori-haswell when setting up test cases or a test suite:

# The paths section describes paths that are used within the ocean core test
# cases.
[paths]

# The root to a location where the mesh_database, initial_condition_database,
# and bathymetry_database for MPAS-Ocean will be cached
ocean_database_root = /global/cfs/cdirs/e3sm/mpas_standalonedata/mpas-ocean

# The root to a location where the mesh_database and initial_condition_database
# for MALI will be cached
landice_database_root = /global/cfs/cdirs/e3sm/mpas_standalonedata/mpas-albany-landice

# the path to the base conda environment where compass environments have
# been created
compass_envs = /global/cfs/cdirs/e3sm/software/compass/cori-haswell/base


# Options related to deploying a compass conda environment on supported
# machines
[deploy]

# the compiler set to use for system libraries and MPAS builds
compiler = intel

# the system MPI library to use for intel compiler
mpi_intel = mpt

# the system MPI library to use for gnu compiler
mpi_gnu = mpt

# the base path to system libraries to be added as part of setting up compass
system_libs = /global/cfs/cdirs/e3sm/software/compass/cori-haswell/system

# the version of ESMF to build if using system compilers and MPI (don't build)
esmf = None

Additionally, some relevant config options come from the mache package:

# The parallel section describes options related to running jobs in parallel
[parallel]

# parallel system of execution: slurm, cobalt or single_node
system = slurm

# whether to use mpirun or srun to run a task
parallel_executable = srun

# cores per node on the machine
cores_per_node = 32

# account for running diagnostics jobs
account = e3sm

# available configurations(s) (default is the first)
configurations = haswell

# quality of service (default is the first)
qos = regular, premium, debug

Intel on Cori-Haswell¶

To load the compass environment and modules, and set appropriate environment variables:

source /global/cfs/cdirs/e3sm/software/compass/cori-haswell/load_latest_compass_intel_mpt.sh

To build the MPAS model with

make intel-nersc

Gnu on Cori-Haswell¶

To load the compass environment and modules, and set appropriate environment variables:

source /global/cfs/cdirs/e3sm/software/compass/cori-haswell/load_latest_compass_gnu_mpt.sh

To build the MPAS model with

make gnu-nersc

Cori-KNL¶

config options¶

And here are the same for -m cori-knl:

# The paths section describes paths that are used within the ocean core test
# cases.
[paths]

# The root to a location where the mesh_database, initial_condition_database,
# and bathymetry_database for MPAS-Ocean will be cached
ocean_database_root = /global/cfs/cdirs/e3sm/mpas_standalonedata/mpas-ocean

# The root to a location where the mesh_database and initial_condition_database
# for MALI will be cached
landice_database_root = /global/cfs/cdirs/e3sm/mpas_standalonedata/mpas-albany-landice

# the path to the base conda environment where compass environments have
# been created
compass_envs = /global/cfs/cdirs/e3sm/software/compass/cori-knl/base


# Options related to deploying a compass conda environment on supported
# machines
[deploy]

# the compiler set to use for system libraries and MPAS builds
compiler = intel

# the system MPI library to use for intel compiler
mpi_intel = impi

# the system MPI library to use for gnu compiler
mpi_gnu = mpt

# the base path to system libraries to be added as part of setting up compass
system_libs = /global/cfs/cdirs/e3sm/software/compass/cori-knl/system

# the version of ESMF to build if using system compilers and MPI (don't build)
esmf = None

Additionally, some relevant config options come from the mache package:

# The parallel section describes options related to running jobs in parallel
[parallel]

# parallel system of execution: slurm, cobalt or single_node
system = slurm

# whether to use mpirun or srun to run a task
parallel_executable = srun

# cores per node on the machine
cores_per_node = 68

# account for running diagnostics jobs
account = e3sm

# available configurations(s) (default is the first)
configurations = knl

# quality of service (default is the first)
qos = regular, premium, debug

Intel on Cori-KNL¶

To load the compass environment and modules, and set appropriate environment variables:

source /global/cfs/cdirs/e3sm/software/compass/cori-knl/load_latest_compass_intel_impi.sh

To build the MPAS model with

make intel-nersc

Jupyter notebook on remote data¶

You can run Jupyter notebooks on NERSC with direct access to scratch data as follows:

ssh -Y -L 8844:localhost:8844 MONIKER@cori.nersc.gov
jupyter notebook --no-browser --port 8844
# in local browser, go to:
http://localhost:8844/

Note that on NERSC, you can also use their Jupyter server, it’s really nice and grabs a compute node for you automatically on logon. You’ll need to create a python kernel from e3sm-unified following these steps (taken from https://docs.nersc.gov/connect/jupyter/). After creating the kernel, you just go to “Change Kernel” in the Jupyter notebook and you’re ready to go.

You can use one of our default Python 2, Python 3, or R kernels. If you have a Conda environment, depending on how it is installed, it may just show up in the list of kernels you can use. If not, use the following procedure to enable a custom kernel based on a Conda environment. Let’s start by assuming you are a user with username user who wants to create a Conda environment on Cori and use it from Jupyter.

module load python
conda create -n myenv python=3.7 ipykernel <further-packages-to-install>
<... installation messages ...>
source activate myenv
python -m ipykernel install --user --name myenv --display-name MyEnv
   Installed kernelspec myenv in /global/u1/u/user/.local/share/jupyter/kernels/myenv

Be sure to specify what version of Python interpreter you want installed. This will create and install a JSON file called a “kernel spec” in kernel.json at the path described in the install command output.

{
    "argv": [
        "/global/homes/u/user/.conda/envs/myenv/bin/python",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
    ],
    "display_name": "MyEnv",
    "language": "python"
}